Built on

Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom · 2013
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
Original
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2014
Earlier work this paper cites.
Video (language) modeling: a baseline for generative models of natural videos
Original
Marc’Aurelio Ranzato, Arthur Szlam, Joan Bruna, Michaël Mathieu, Ronan Collobert, and Sumit Chopra · 2014
Earlier work this paper cites.
Deep multi-scale video prediction beyond mean square error
Original
Michaël Mathieu, Camille Couprie, and Yann LeCun · 2015
Earlier work this paper cites.

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Unsupervised learning for physical interaction through video prediction
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Chelsea Finn, Ian J. Goodfellow, and Sergey Levine · 2016
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Identity mappings in deep residual networks
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2016

Video Pixel Networks

Kalchbrenner, Nal, Oord, Aaron van den, Simonyan, Karen et al.

Understand

We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video.

The model and the neural architecture reflect the time, space and color structure of video tensors and encode it as a four-dimensional dependency chain.
The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth.
The VPN also produces detailed samples on the action-conditional Robotic Pushing benchmark and generalizes to the motion of novel objects.

Built on

Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom · 2013
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
Original
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2014
Earlier work this paper cites.
Video (language) modeling: a baseline for generative models of natural videos
Original
Marc’Aurelio Ranzato, Arthur Szlam, Joan Bruna, Michaël Mathieu, Ronan Collobert, and Sumit Chopra · 2014
Earlier work this paper cites.
Deep multi-scale video prediction beyond mean square error
Original
Michaël Mathieu, Camille Couprie, and Yann LeCun · 2015
Earlier work this paper cites.

Similar

Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, and Satinder P. Singh · 2015
Cited alongside, same era.
Spatio-temporal video autoencoder with differentiable memory
Original
Viorica Patraucean, Ankur Handa, and Roberto Cipolla · 2015
Cited alongside, same era.
Convolutional LSTM network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
Original
Fisher Yu and Vladlen Koltun · 2015
Cited alongside, same era.
Dynamic filter networks
Original
Bert De Brabandere, Xu Jia, Tinne Tuytelaars, and Luc Van Gool · 2016

Then

Unsupervised learning for physical interaction through video prediction
Original
Chelsea Finn, Ian J. Goodfellow, and Sergey Levine · 2016
Closest in time.
Identity mappings in deep residual networks
Original
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Closest in time.
Grid long short-term memory
Nal Kalchbrenner, Ivo Danihelka, and Alex Graves · 2016
Closest in time.
Generating videos with scene dynamics
Original
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
Closest in time.